Sub-wavelength imaging with hyperbolic metamaterials

http://tartu.ester.ee/record=b2693546~S1*es

Dark-field hyperlens: Super-resolution imaging of weakly scattering objects

We propose and numerically demonstrate a technique for subwavelength imaging based on a metal-dielectric multilayer hyperlens designed in such a way that only the large-wavevector waves are transmitted while all propagating waves from the image area are blocked by the hyperlens. As a result, the image plane only contains scattered light from subwavelength features of the objects and is free from background illumination. Similar in spirit to conventional dark-field microscopy, the proposed dark-field hyperlens is promising for optical imaging of weakly scattering subwavelength objects, such as optical nanoscopy of label-free biological objects.Comment: 6 figure

Pseudocanalization regime for magnetic dark-field hyperlens

Hyperbolic metamaterials (HMMs) are the cornerstone of the hyperlens, which brings the superresolution effect from the near-field to the far-field zone. For effective application of the hyperlens it should operate in so-called canalization regime, when the phase advancement of the propagating fields is maximally supressed, and thus field broadening is minimized. For conventional hyperlenses it is relatively straightforward to achieve canalization by tuning the anisotropic permittivity tensor. However, for a dark-field hyperlens designed to image weak scatterers by filtering out background radiation (dark-field regime) this approach is not viable, because design requirements for such filtering and elimination of phase advancement i.e. canalization, are mutually exclusive. Here we propose the use of magnetic ($\mu$-positive and negative) HMMs to achieve phase cancellation at the output equivalent to the performance of a HMM in the canalized regime. The proposed structure offers additional flexibility over simple HMMs in tuning light propagation. We show that in this ``pseudocanalizing'' configuration quality of an image is comparable to a conventional hyperlens, while the desired filtering of the incident illumination associated with the dark-field hyperlens is preserved

Wave Front Tuning of Coupled Hyperbolic Surface Waves on Anisotropic Interfaces

A photonic surface wave, a propagating optical mode localized at the interface of two media, can play a significant role in controlling the flow of light at nanoscale. Among various types of such waves, surface waves with hyperbolic dispersion or simply hyperbolic surface waves supported on anisotropic metal interfaces can be exploited to effectively control the propagation of lightwaves. We used semi-analytical and numerical methods to study the nature of surface waves on several configurations of three-layers metal–dielectric–metal systems including isotropic and anisotropic cases where the metal cladding layers were assumed to have infinite thickness. We used semi-analytical and numerical approaches to study the phenomena. We showed that the propagation of surface wave can be tuned from diverging to converging in the plane of the interface by the combination of metals with different anisotropic properties

A neural operator-based surrogate solver for free-form electromagnetic inverse design

Neural operators have emerged as a powerful tool for solving partial differential equations in the context of scientific machine learning. Here, we implement and train a modified Fourier neural operator as a surrogate solver for electromagnetic scattering problems and compare its data efficiency to existing methods. We further demonstrate its application to the gradient-based nanophotonic inverse design of free-form, fully three-dimensional electromagnetic scatterers, an area that has so far eluded the application of deep learning techniques

Inverse design of core-shell particles with discrete material classes using neural networks

The design of scatterers on demand is a challenging task that requires the investigation and development of novel and flexible approaches. In this paper, we propose a machine learning-assisted optimization framework to design multi-layered core-shell particles that provide a scattering response on demand. Artificial neural networks can learn to predict the scattering spectrum of core-shell particles with high accuracy and can act as fully differentiable surrogate models for a gradient-based design approach. To enable the fabrication of the particles, we consider existing materials and introduce a novel two-step optimization to treat continuous geometric parameters and discrete feasible materials simultaneously. Moreover, we overcome the non-uniqueness of the problem and expand the design space to particles of varying numbers of shells, i.e., different number of optimization parameters, with a classification network. Our method is 1–2 orders of magnitudes faster than conventional approaches in both forward prediction and inverse design and is potentially scalable to even larger and more complex scatterers